Influence of nicotine concentration and flavours on mouth level exposure and puffing topography among regular e-cigarette consumers in New Zealand.

Falls during hospitalization are among the most frequently reported adverse events in acute care settings, posing substantial threats to patient safety and creating considerable clinical and economic burdens for healthcare systems. Approximately 3%–5% of hospitalized patients experience a fall during their stay, with consequences ranging from minor injuries to severe complications, including fractures, head trauma, prolonged hospitalization, and increased mortality. Beyond direct patient harm, falls are associated with extended hospital stays, increased healthcare costs, and reduced patient and family satisfaction. Consequently, fall prevention is widely recognized by major regulatory and accreditation organizations as a key indicator of hospital quality and patient safety worldwide. Therefore, early identification of patients at high risk and timely implementation of preventive strategies are critical to improving clinical outcomes and optimizing healthcare resource utilization. Current clinical fall risk assessment in hospitalized patients primarily relies on structured variables, such as age, diagnosis, mobility, medication use, and cognitive status, along with standardized tools including the Morse Fall Scale and the Hendrich II Fall Risk Model. However, these conventional approaches demonstrate limited sensitivity and specificity in predicting actual falls; multicenter studies report area under the curve (AUC) values typically below 0.70, reflecting suboptimal discriminatory performance. This limitation highlights the need for more comprehensive and dynamic approaches that better capture patient-specific risk factors. Nurses, who spend more time in direct contact with hospitalized patients than any other healthcare professionals, are uniquely positioned to observe subtle changes in patients’ functional status, behavior, gait, and environmental context that influence fall risk. Nursing documentation—including routine assessments, functional observations, interventions, and patient responses—captures these bedside insights. Many hospitals have recently adopted structured or semi-structured nursing documentation systems that allow nurses to record standardized clinical observations using predefined items or coded statements. These data provide detailed, context-specific information on risk factors, such as mobility impairment, balance difficulties, and confusion, as well as nursing interventions and situational observations (e.g., “patient unsteady on feet,” “frequent toileting needs”) that extend beyond what diagnostic and procedural codes alone can capture.